FireRedASR
F
Fireredasr
Overview :
FireRedASR is an open-source industrial-grade Mandarin automatic speech recognition model, utilizing an Encoder-Decoder and LLM integrated architecture. It includes two variants: FireRedASR-LLM and FireRedASR-AED, designed for high-performance and efficient needs respectively. The model excels in Mandarin benchmarking tests and also performs well in recognizing dialects and English speech. It is suitable for industrial applications requiring efficient speech-to-text conversion, such as smart assistants and video subtitle generation. The open-source model is easy for developers to integrate and optimize.
Target Users :
This product is ideal for enterprises and developers needing efficient speech-to-text conversion, particularly those working in fields such as smart assistants, video subtitles generation, and voice interaction applications. Its open-source nature also makes it suitable for technical teams looking to customize their development.
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Use Cases
Implement voice command recognition and interaction in smart voice assistants
Automatically generate accurate subtitle content for video platforms
Achieve speech-to-text conversion for Mandarin and dialects in multilingual environments
Features
Employs an Encoder-Adapter-LLM framework for end-to-end speech interaction
Supports multiple Mandarin scenarios such as video, live broadcasts, and smart assistants
Achieves low Character Error Rate (CER) in Mandarin benchmarking tests
Offers a compact model architecture, suitable for resource-constrained applications
Supports dialect and English speech recognition, expanding application scenarios
Open-source model and inference code facilitate developer integration and optimization
Excels in recognizing singing lyrics, suitable for music-related applications
How to Use
Visit the project homepage to download the open-source code and model files
Choose between the FireRedASR-LLM or FireRedASR-AED model based on your needs
Use the provided inference code to conduct speech recognition tests
Integrate the model into your application to enable speech-to-text functionality
Adjust model parameters according to practical application scenarios to optimize performance
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